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Article

Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm

1
Unit of Electrical Engineering, Tampere University, 33720 Tampere, Finland
2
Department of Electrical Engineering, Uppsala University, 75237 Uppsala, Sweden
3
Department of Communications and Networking, Aalto University, 02150 Espoo, Finland
*
Author to whom correspondence should be addressed.
Academic Editor: Gregor Klančar
Sensors 2021, 21(16), 5549; https://doi.org/10.3390/s21165549
Received: 6 July 2021 / Revised: 12 August 2021 / Accepted: 13 August 2021 / Published: 18 August 2021
(This article belongs to the Section Sensor Networks)
Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm’s potential, a novel localization-and-tracking system is presented to estimate a target’s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy. View Full-Text
Keywords: received signal strength; localization and tracking; bayesian filtering and smoothing; parameter estimation; expectation-maximization algorithm received signal strength; localization and tracking; bayesian filtering and smoothing; parameter estimation; expectation-maximization algorithm
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MDPI and ACS Style

Kaltiokallio, O.; Hostettler, R.; Yiğitler, H.; Valkama, M. Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm. Sensors 2021, 21, 5549. https://doi.org/10.3390/s21165549

AMA Style

Kaltiokallio O, Hostettler R, Yiğitler H, Valkama M. Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm. Sensors. 2021; 21(16):5549. https://doi.org/10.3390/s21165549

Chicago/Turabian Style

Kaltiokallio, Ossi, Roland Hostettler, Hüseyin Yiğitler, and Mikko Valkama. 2021. "Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm" Sensors 21, no. 16: 5549. https://doi.org/10.3390/s21165549

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